Research Project: Improved Aerial Application Technologies for Precise and Effective Delivery of Crop Production Products

Location: Aerial Application Technology Research

Publications (Clicking on the reprint icon will take you to the publication reprint.)

Spray coverage and deposits from a remotely piloted aerial application system using various nozzle types - (Peer Reviewed Journal)
Butts, T.R., Fritz, B.K., Davis, J.A., Spurlock, T.N. 2024. Spray coverage and deposits from a remotely piloted aerial application system using various nozzle types. Frontiers in Agronomy. 6:1493799. https://doi.org/10.3389/fagro.2024.1493799.

Granular bait applications for management of rangeland grasshoppers using a remotely piloted aerial application system - (Peer Reviewed Journal)
Rodriguez, R., Woller, D.A., Martin, D.E., Reuter, K.C., Black, L.R., Latheef, M.A., Lopez-Colon, K.M., Taylor, M. 2024. Granular bait applications for management of rangeland grasshoppers using a remotely piloted aerial application system. Drones. 8:535. https://doi.org/10-3390/drones8100535.

Adjuvant and pesticide formulation interactions: Impacts on spray quality in ground and aerial applications - (Peer Reviewed Journal)

Influence of UAV rotor wash on spray droplet atomization: Implications for pesticide application and environmental risk - (Peer Reviewed Journal)

Developments in airborne and satellite remote sensors for precision agriculture - (Book / Chapter)

AI-driven computer vision detection of cotton in corn fields using UAS remote sensing data and spot-spray application - (Peer Reviewed Journal)
Yadav, P.K., Thomasson, J.A., Hardin, R., Searcy, S.W., Braga-Neto, U., Popescu, S.C., Rodriguez, R., Martin, D.E., Enciso, J. 2024. AI-driven computer vision detection of cotton in corn fields using UAS remote sensing data and spot-spray application. Remote Sensing. 16:2754. https://doi.org/10.3390/rs16152754.

Spray drift characterization of a remotely piloted aerial application system - (Peer Reviewed Journal)
Martin, D.E., Tang, Z., Yang, Y., Latheef, M.A., Fritz, B.K., Kruger, G., Houston, T. 2024. Spray drift characterization of a remotely piloted aerial application system. Applied Engineering in Agriculture. 40(4):385-399. https://doi.org/10.13031/aea.15605.

Practical methods for aerial image acquisition and reflectance conversion using consumer-grade cameras on manned and unmanned aircraft - (Peer Reviewed Journal)
Yang, C., Fritz, B.K., Suh, C.P. 2024. Practical methods for aerial image acquisition and reflectance conversion using consumer-grade cameras on manned and unmanned aircraft. Precision Agriculture. https://doi.org/10.1007/s11119-024-10145-w.

Spray drift of mosquito larvicide applied with a multirotor drone - (Proceedings)
Thistle, A., Bonds, J., Fritz, B.K., Thistle, H. 2024. Spray drift of mosquito larvicide applied with a multirotor drone. Proceedings Annual Conference on Liquid Atomization and Spray Systems. 2024-59.

Field study to assess UASS adulticiding Part 1: Methods and data - (Peer Reviewed Journal)
Bonds, J.A., Thistle, H., Fritz, B.K., Jank, P.C., Martin, D.E., Reynolds, W. 2024. Field study to assess UASS adulticiding Part 1: Methods and data. Journal of the ASABE. 67(5):1145-1164. https://doi.org/10.13031/ja.15785.

Uncrewed aerial spray systems for mosquito control: Efficacy studies for space sprays - (Peer Reviewed Journal)
Bonds, J.A., Thistle, H., Fritz, B.K., Reynolds, W., Kimbell, P. 2023. Uncrewed aerial spray systems for mosquito control: Efficacy studies for space sprays. Journal of the American Mosquito Control Association. 39(4):223-230. https://doi.org/10.2987/23-7140.

Applying machine learning classifiers to Sentinel-2 imagery for early identification of cotton fields to advance boll weevil eradication - (Peer Reviewed Journal)
Yang, C., Suh, C.P. 2023. Applying machine learning classifiers to Sentinel-2 imagery for early identification of cotton fields to advance boll weevil eradication. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.108268.

Uncrewed aerial vehicle radiometric calibration: A comparison of auto exposure and fixed exposure images - (Peer Reviewed Journal)
Bagnall, G.C., Thomasson, J.A., Yang, C., Wang, T., Han, X., Sima, C., Chang, A. 2023. Uncrewed aerial vehicle radiometric calibration: A comparison of auto exposure and fixed exposure images. Journal of Applied Remote Sensing (JARS). https://doi.org/10.1002/ppj2.20082.

Comparisons between temporal statistical metrics, time series stacks and phenological features derived from NASA Harmonized Landsat Sentinel-2 data for crop type mapping - (Peer Reviewed Journal)
Liu, X., Xie, S., Yang, J., Sun, L., Liu, L., Zhang, Q., Yang, C. 2023. Comparisons between temporal statistical metrics, time series stacks and phenological features derived from NASA Harmonized Landsat Sentinel-2 data for crop type mapping. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.108015.

Straight stream nozzles models to support aerial applications - (Peer Reviewed Journal)

Evaluation of spatial resolution on crop disease detection based on multiscale images and category variance ratio - (Peer Reviewed Journal)
Zhao, H., Yang, Y., Yang, C., Song, R., Guo, W. 2023. Evaluation of spatial resolution on crop disease detection based on multiscale images and category variance ratio. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.107743.

Practical methods for aerial image acquisition and reflectance conversion using consumer cameras - (Proceedings)
Yang, C., Fritz, B.K., Suh, C.P. 2023. Practical methods for aerial image acquisition and reflectance conversion using consumer cameras. European Conference on Precision Agriculture Proceedings. pp. 1027-1034. https://doi.org/10.3920/978-90-8686-947-3_129.

Calculation of swath width and swath displacement for uncrewed aerial spray systems - (Peer Reviewed Journal)
Bonds, J., Fritz, B.K., Thistle, H. 2023. Calculation of swath width and swath displacement for uncrewed aerial spray systems. Applied Engineering in Agriculture. 66(3):523-532. https://doi.org/10.13031/ja.15400.

Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery - (Peer Reviewed Journal)
Yadav, P.K., Thomasson, J.A., Hardin, R., Searcy, S.W., Braga-Neto, U., Popescu, S.C., Martin, D.E., Rodriguez, R., Meza, K., Encisco, J., Diaz, J.S., Wang, T. 2022. Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery. Computers in Agriculture. https://doi.org/10.1016/j.compag.2022.107551.

Herbicide spray drift from ground and aerial applications: Implications for potential pollinator foraging sources - (Peer Reviewed Journal)
Butts, T.R., Fritz, B.K., Kouame, B., Norsworthy, J.K., Barber, L.T., Ross, J., Lorenz, G.M., Thrash, B.C., Bateman, N.R., Adamczyk Jr, J.J. 2022. Herbicide spray drift from ground and aerial applications: Implications for potential pollinator foraging sources. Nature Plants. https://doi.org/10.1038/s41598-022-22916-4.

The physics of spray droplets released from ag aircraft - (Popular Publication)
Fritz, B.K. 2022. The physics of spray droplets released from ag aircraft. Agricultural Aviation. 49(4):44-49.

The physics of spray droplets released from ag aircraft - (Popular Publication)
Fritz, B.K. 2022. The physics of spray droplets released from ag aircraft. Agricultural Aviation. 49(4):44-49.

Insecticidal management of rangeland grasshoppers using a remotely piloted aerial application system - (Peer Reviewed Journal)
Martin, D.E., Rodriguez, R., Woller, D.A., Reuter, K.C., Black, L.R., Latheef, M.A., Taylor, M., Lopez Colon, K.M. 2022. Insecticidal management of rangeland grasshoppers using a remotely piloted aerial application system. Drones. https://doi.org/10.3390/drones6090239.

Payload capacities of remotely piloted aerial application systems affect spray pattern and effective swath - (Peer Reviewed Journal)
Martin, D.E., Latheef, M.A. 2022. Payload capacities of remotely piloted aerial application systems affect spray pattern and effective swath. Drones. https://doi.org/10.3390/drones6080205.

Panicle Ratio Network: Streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field - (Peer Reviewed Journal)
Guo, Z., Yang, C., Yang, W., Chen, G., Jiang, Z., Wang, B., Zhang, J. 2022. Panicle Ratio Network: Streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field. Journal of Experimental Botany. 71(19):6575-6588.

A novel composite vegetation index including solar-induced chlorophyll fluorescence for seedling rapeseed net photosynthesis rate retrieval - (Peer Reviewed Journal)
Zhang, J., Sun, B., Yang, C., Wang, C., You, Y., Zhou, G., Liu, B., Wang, C., Kuai, J., Xie, J. 2022. A novel composite vegetation index including solar-induced chlorophyll fluorescence for seedling rapeseed net photosynthesis rate retrieval. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107031.

Gap optimization of electrostatic aerial spray nozzles for low-speed aircraft - (Peer Reviewed Journal)
Martin, D.E., Latheef, M.A., Duke, S.E. 2022. Gap optimization of electrostatic aerial spray nozzles for low-speed aircraft. Journal of Electrostatics. https://doi.org/10.1016/j.elstat.2022.103714.

Winter wheat nitrogen estimation based on ground-level and UAV-mounted sensors - (Peer Reviewed Journal)
Song, X., Yang, G., Xu, X., Zhang, D., Yang, C., Feng, H. 2022. Winter wheat nitrogen estimation based on ground-level and UAV-mounted sensors. Sensors. https://doi.org/10.3390/s22020549.

Spray drift potential of dicamba plus S-metolachlor formulations - (Peer Reviewed Journal)
Vieira, B., Alves, G., Vukoja, B., Vinicius, V., Zaric, M., Houston, T., Fritz, B.K., Kruger, G. 2022. Spray drift potential of dicamba plus S-metolachlor formulations. Pest Management Science. https://doi.org/doi 10.1002/ps.6772.

Standardizing agricultural spray droplet size distributions - (Peer Reviewed Journal)
Fritz, B.K., Sun, S., Kruger, G. 2022. Standardizing agricultural spray droplet size distributions. American Society for Testing and Materials. http://doi.org/10.1520/STP164120210077.

GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing - (Peer Reviewed Journal)
Wang, T., Mei, X., Thomasson, A., Yang, C., Han, X., Yadav, P., Shi, Y. 2021. GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing. Computers and Electronics in Agriculture. 193. Article 106629. https://doi.org/10.1016/j.compag.2021.106629.

A field programme for the spray distribution of Unmanned Aerial Spray Systems (UASS) and the development of larvicide systems for vector control - (Peer Reviewed Journal)
Bonds, J., Fritz, B.K., Thistle, H. 2022. A field programme for the spray distribution of Unmanned Aerial Spray Systems (UASS) and the development of larvicide systems for vector control. Aspects of Applied Biology. 147:105-115.

Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress - (Peer Reviewed Journal)
Jiang, Z., Tu, H., Bai, B., Yang, C., Zhao, B., Guo, Z., Liu, Q., Zhao, H., Yang, W., Xiaon, L., Zhang, J. 2021. Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress. New Phytologist. 232(1):440-455. https://doi.org/10.1111/nph.17580.

Crop height estimation based on UAV images: methods, errors, and strategies - (Peer Reviewed Journal)
Xie, T., Li, J., Yang, C., Jiang, Z., Chen, Y., Guo, L., Zhang, J. 2021. Crop height estimation based on UAV images: Methods, errors, and strategies. Computers and Electronics in Agriculture. 185:106155. https://doi.org/10.1016/j.compag.2021.106155.

Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter - (Peer Reviewed Journal)
Sun, B., Wang, C., Yang, C., Xu, B., Kuai, J., Li, X., Xu, S., Liu, B., Xie, T., Zhou, G., Zhang, J. 2021. Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter. International Journal of Applied Earth Observation and Geoinformation. 102:1-10. https://doi.org/10.1016/j.jag.2021.102373.

Utilization of unmanned aerial systems in mosquito and vector control programs - (Peer Reviewed Journal)
Faraji, A., Sorensen, B., Haas-Stapleton, E., Scholl, M., Goodman, B., Buettner, J., Schon, S., Ortiz, E., Hartle, J., Lefkow, N., Lewis, C., Fritz, B.K., Hoffmann, W., Williams, G. 2021. Utilization of unmanned aerial systems in mosquito and vector control programs. Journal of Economic Entomology. 114(5):1896-1909. https://doi.org/10.1093/jee/toab107.

Comparison of Sentinel-2, Landsat, and airborne imagery for identification of cotton fields for boll weevil eradication - (Proceedings)
Yang, C., Suh, C.P. 2021. Comparison of Sentinel-2, Landsat, and airborne imagery for identification of cotton fields for boll weevil eradication. National Cotton Council Beltwide Cotton Conference. pp. 234-239. CD-ROM.

Identification of cotton root rot by multifeature selection from Sentinel-2 images using random forest - (Peer Reviewed Journal)
Li, X., Yang, C., Huang, W., Tang, J., Tian, Y., Zhang, Q. 2020. Identification of cotton root rot by multifeature selection from Sentinel-2 images using random forest. Remote Sensing. 12:3504. https://doi.org/10.3390/rs12213504.

Rapeseed stand count estimation at leaf development stages with UAV imagery and convolutional neural networks - (Peer Reviewed Journal)
Zhang, J., Biquan, Z., Yang, C., Yeyin, S., Qingxi, L., Guanscheng, Z., Chufeng, W., Tianjin, X., Zhao, J., Dongyan, Z., Wanneng, Y., Chenglong, H., Jing, X. 2020. Rapeseed stand count estimation at leaf development stages with UAV imagery and convolutional neural networks. Frontiers in Plant Science. 11:617. https://doi.org/10.3389/fpls.2020.00617.

Assessing the effect of real spatial resolution of in situ UAV multispectral images on seedling rapeseed growth monitoring - (Peer Reviewed Journal)
Zhang, J., Wang, C., Yang, C., Xie, T., Jiang, Z., Hu, T., Luo, Z., Zhou, G., Xie, J. 2020. Assessing the effect of real spatial resolution of in situ UAV multispectral images on seedling rapeseed growth monitoring. Remote Sensing. 12:1207. https://doi.org/10.3390/rs12071207.

Unmanned aerial vehicle remote sensing to delineate cotton root rot - (Peer Reviewed Journal)
Wang, T., Thomasson, A., Yang, C., Isakeit, T., Nichols, R.L., Collett, R.M., Han, X., Bagnall, C. 2020. Unmanned aerial vehicle remote sensing to delineate cotton root rot. Journal of Applied Remote Sensing (JARS). 14(3):034522-1. https://doi.org/10.1117/1.jrs.14.034522.

A plant-by-plant-level cotton root rot identification method based on UAV remote sensing - (Peer Reviewed Journal)
Wang, T., Thomasson, A., Isakeit, T., Yang, C. 2020. A plant-by-plant-level cotton root rot identification method based on UAV remote sensing. Remote Sensing. 12:2453. https://doi.org/10.3390/rs12152453.

Aerial application methods for control of weed species in fallow farmlands in Texas - (Peer Reviewed Journal)
Martin, D.E., Latheef, M.A., Lopez, J., Duke, S.E. 2020. Aerial application methods for control of weed species in fallow farmlands in Texas. Agronomy Journal. 10:11.

Spray deposition on weeds (Palmer amaranth and Morningglory) from a remotely piloted aerial application system and backpack sprayer - (Peer Reviewed Journal)
Martin, D.E., Singh, V., Latheef, M.A., Bagavathiannan, M.V. 2020. Spray deposition on weeds (Palmer amaranth and Morningglory) from a remotely piloted aerial application system and backpack sprayer. Drones. 4(3):59. https://doi.org/10.3390/drones4030059.

Remote sensing technologies for crop disease and pest detection - (Book / Chapter)
Yang, C. 2022. Remote sensing technologies for crop disease and pest detection. Book Chapter. p. 159-184. In: Li, M., Yang, C., Zhang, Q. (eds) Soil and Crop Sensing for Precision Crop Production. Agriculture Automation and Control. Springer, Cham. https://doi.org/10.1007/978-3-030-70432-2_6.

Automatic estimation of crop disease severity levels based on vegetation index normalization - (Peer Reviewed Journal)
Zhao, H., Yang, C., Guo, W., Zhang, L., Zhang, D. 2020. Automatic estimation of crop disease severity levels based on vegetation index normalization. Remote Sensing. 12:1930. https://doi.org/10.3390/rs12121930.